Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations10
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory160.0 B

Variable types

TimeSeries10
Categorical3
Numeric6

Timeseries statistics

Number of series10
Time series length10
Starting point1746685620000
Ending point1746686160000
Period60000
2025-05-18T08:26:38.837546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.876964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

high has constant value "98830.3" Constant
close is highly overall correlated with sma_14 and 1 other fieldsHigh correlation
low is highly overall correlated with macdhist_7 and 4 other fieldsHigh correlation
macd_7 is highly overall correlated with macdhist_7 and 6 other fieldsHigh correlation
macdhist_7 is highly overall correlated with low and 6 other fieldsHigh correlation
macdsignal_7 is highly overall correlated with low and 5 other fieldsHigh correlation
obv_7 is highly overall correlated with macd_7 and 6 other fieldsHigh correlation
rsi_14 is highly overall correlated with rsi_21 and 3 other fieldsHigh correlation
rsi_21 is highly overall correlated with rsi_14 and 5 other fieldsHigh correlation
rsi_60 is highly overall correlated with rsi_21 and 4 other fieldsHigh correlation
rsi_7 is highly overall correlated with low and 7 other fieldsHigh correlation
sma_14 is highly overall correlated with close and 8 other fieldsHigh correlation
sma_21 is highly overall correlated with macd_7 and 6 other fieldsHigh correlation
sma_60 is highly overall correlated with rsi_14 and 6 other fieldsHigh correlation
sma_7 is highly overall correlated with macd_7 and 2 other fieldsHigh correlation
target is highly overall correlated with macd_7 and 4 other fieldsHigh correlation
volume is highly overall correlated with macd_7 and 6 other fieldsHigh correlation
window_start_ms is highly overall correlated with close and 8 other fieldsHigh correlation
low is highly imbalanced (53.1%) Imbalance
window_start_ms is non stationary Non stationary
target is non stationary Non stationary
sma_7 is non stationary Non stationary
sma_14 is non stationary Non stationary
sma_21 is non stationary Non stationary
rsi_21 is non stationary Non stationary
window_start_ms is uniformly distributed Uniform
close is uniformly distributed Uniform
sma_14 is uniformly distributed Uniform
window_start_ms has unique values Unique
volume has unique values Unique
sma_14 has unique values Unique
sma_21 has unique values Unique
sma_60 has unique values Unique
rsi_14 has unique values Unique
macd_7 has unique values Unique
macdsignal_7 has unique values Unique
macdhist_7 has unique values Unique
obv_7 has unique values Unique

Reproduction

Analysis started2025-05-18 06:26:33.379455
Analysis finished2025-05-18 06:26:38.822576
Duration5.44 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

window_start_ms
Numeric time series

High correlation  Non stationary  Uniform  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7466859 × 1012
Minimum1.7466856 × 1012
Maximum1.7466862 × 1012
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-18T08:26:38.916511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.7466856 × 1012
5-th percentile1.7466856 × 1012
Q11.7466858 × 1012
median1.7466859 × 1012
Q31.746686 × 1012
95-th percentile1.7466861 × 1012
Maximum1.7466862 × 1012
Range540000
Interquartile range (IQR)270000

Descriptive statistics

Standard deviation181659.02
Coefficient of variation (CV)1.0400211 × 10-7
Kurtosis-1.2
Mean1.7466859 × 1012
Median Absolute Deviation (MAD)150000
Skewness0
Sum1.7466859 × 1013
Variance3.3 × 1010
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0.9585320756
2025-05-18T08:26:38.938392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-18T08:26:39.000053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-18T08:26:39.013838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.74668562 × 10121
10.0%
1.74668568 × 10121
10.0%
1.74668574 × 10121
10.0%
1.7466858 × 10121
10.0%
1.74668586 × 10121
10.0%
1.74668592 × 10121
10.0%
1.74668598 × 10121
10.0%
1.74668604 × 10121
10.0%
1.7466861 × 10121
10.0%
1.74668616 × 10121
10.0%
ValueCountFrequency (%)
1.74668562 × 10121
10.0%
1.74668568 × 10121
10.0%
1.74668574 × 10121
10.0%
1.7466858 × 10121
10.0%
1.74668586 × 10121
10.0%
1.74668592 × 10121
10.0%
1.74668598 × 10121
10.0%
1.74668604 × 10121
10.0%
1.7466861 × 10121
10.0%
1.74668616 × 10121
10.0%
ValueCountFrequency (%)
1.74668616 × 10121
10.0%
1.7466861 × 10121
10.0%
1.74668604 × 10121
10.0%
1.74668598 × 10121
10.0%
1.74668592 × 10121
10.0%
1.74668586 × 10121
10.0%
1.7466858 × 10121
10.0%
1.74668574 × 10121
10.0%
1.74668568 × 10121
10.0%
1.74668562 × 10121
10.0%
2025-05-18T08:26:38.955713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

open
Numeric time series

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98830.27
Minimum98830.2
Maximum98830.3
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-18T08:26:39.038882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98830.2
5-th percentile98830.2
Q198830.225
median98830.3
Q398830.3
95-th percentile98830.3
Maximum98830.3
Range0.1
Interquartile range (IQR)0.075

Descriptive statistics

Standard deviation0.048304589
Coefficient of variation (CV)4.887631 × 10-7
Kurtosis-1.2244898
Mean98830.27
Median Absolute Deviation (MAD)0
Skewness-1.0350983
Sum988302.7
Variance0.0023333333
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0003766100556
2025-05-18T08:26:39.058770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
2025-05-18T08:26:39.117198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-18T08:26:39.131135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98830.3 7
70.0%
98830.2 3
30.0%
ValueCountFrequency (%)
98830.2 3
30.0%
98830.3 7
70.0%
ValueCountFrequency (%)
98830.3 7
70.0%
98830.2 3
30.0%
2025-05-18T08:26:39.075393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

high
Categorical

Constant 

Distinct1
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
98830.3
10 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row98830.3
2nd row98830.3
3rd row98830.3
4th row98830.3
5th row98830.3

Common Values

ValueCountFrequency (%)
98830.3 10
100.0%

Length

2025-05-18T08:26:39.155251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T08:26:39.169617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98830.3 10
100.0%

Most occurring characters

ValueCountFrequency (%)
8 20
28.6%
3 20
28.6%
9 10
14.3%
0 10
14.3%
. 10
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 20
28.6%
3 20
28.6%
9 10
14.3%
0 10
14.3%
. 10
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 20
28.6%
3 20
28.6%
9 10
14.3%
0 10
14.3%
. 10
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 20
28.6%
3 20
28.6%
9 10
14.3%
0 10
14.3%
. 10
14.3%

low
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
98830.2
98830.3

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row98830.2
2nd row98830.2
3rd row98830.2
4th row98830.2
5th row98830.2

Common Values

ValueCountFrequency (%)
98830.2 9
90.0%
98830.3 1
 
10.0%

Length

2025-05-18T08:26:39.184752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T08:26:39.197580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98830.2 9
90.0%
98830.3 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
8 20
28.6%
3 11
15.7%
9 10
14.3%
0 10
14.3%
. 10
14.3%
2 9
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 20
28.6%
3 11
15.7%
9 10
14.3%
0 10
14.3%
. 10
14.3%
2 9
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 20
28.6%
3 11
15.7%
9 10
14.3%
0 10
14.3%
. 10
14.3%
2 9
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 20
28.6%
3 11
15.7%
9 10
14.3%
0 10
14.3%
. 10
14.3%
2 9
12.9%

close
Categorical

High correlation  Uniform 

Distinct2
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
98830.3
98830.2

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row98830.3
2nd row98830.2
3rd row98830.2
4th row98830.2
5th row98830.3

Common Values

ValueCountFrequency (%)
98830.3 5
50.0%
98830.2 5
50.0%

Length

2025-05-18T08:26:39.215001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T08:26:39.229128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98830.3 5
50.0%
98830.2 5
50.0%

Most occurring characters

ValueCountFrequency (%)
8 20
28.6%
3 15
21.4%
9 10
14.3%
0 10
14.3%
. 10
14.3%
2 5
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 20
28.6%
3 15
21.4%
9 10
14.3%
0 10
14.3%
. 10
14.3%
2 5
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 20
28.6%
3 15
21.4%
9 10
14.3%
0 10
14.3%
. 10
14.3%
2 5
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 20
28.6%
3 15
21.4%
9 10
14.3%
0 10
14.3%
. 10
14.3%
2 5
 
7.1%

target
Numeric time series

High correlation  Non stationary 

Distinct5
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98837.87
Minimum98830.2
Maximum98850
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-18T08:26:39.248508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98830.2
5-th percentile98830.2
Q198830.225
median98830.3
Q398849.25
95-th percentile98850
Maximum98850
Range19.8
Interquartile range (IQR)19.025

Descriptive statistics

Standard deviation9.8675732
Coefficient of variation (CV)9.9835955 × 10-5
Kurtosis-2.2125217
Mean98837.87
Median Absolute Deviation (MAD)0.1
Skewness0.50508599
Sum988378.7
Variance97.369
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.8932926698
2025-05-18T08:26:39.272737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
2025-05-18T08:26:39.332841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-18T08:26:39.346965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98830.3 3
30.0%
98830.2 3
30.0%
98850 2
20.0%
98847.3 1
 
10.0%
98849.9 1
 
10.0%
ValueCountFrequency (%)
98830.2 3
30.0%
98830.3 3
30.0%
98847.3 1
 
10.0%
98849.9 1
 
10.0%
98850 2
20.0%
ValueCountFrequency (%)
98850 2
20.0%
98849.9 1
 
10.0%
98847.3 1
 
10.0%
98830.3 3
30.0%
98830.2 3
30.0%
2025-05-18T08:26:39.289289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

volume
Real number (ℝ)

High correlation  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10331377
Minimum0.01253976
Maximum0.50743402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size160.0 B
2025-05-18T08:26:39.366543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01253976
5-th percentile0.014117447
Q10.020950915
median0.030536955
Q30.13005896
95-th percentile0.35457085
Maximum0.50743402
Range0.49489426
Interquartile range (IQR)0.10910804

Descriptive statistics

Standard deviation0.15273461
Coefficient of variation (CV)1.4783567
Kurtosis6.4757983
Mean0.10331377
Median Absolute Deviation (MAD)0.01624421
Skewness2.4574753
Sum1.0331377
Variance0.02332786
MonotonicityNot monotonic
2025-05-18T08:26:39.385592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0204996 1
10.0%
0.01604573 1
10.0%
0.02588938 1
10.0%
0.03518453 1
10.0%
0.50743402 1
10.0%
0.16773809 1
10.0%
0.07813467 1
10.0%
0.14736705 1
10.0%
0.02230486 1
10.0%
0.01253976 1
10.0%
ValueCountFrequency (%)
0.01253976 1
10.0%
0.01604573 1
10.0%
0.0204996 1
10.0%
0.02230486 1
10.0%
0.02588938 1
10.0%
0.03518453 1
10.0%
0.07813467 1
10.0%
0.14736705 1
10.0%
0.16773809 1
10.0%
0.50743402 1
10.0%
ValueCountFrequency (%)
0.50743402 1
10.0%
0.16773809 1
10.0%
0.14736705 1
10.0%
0.07813467 1
10.0%
0.03518453 1
10.0%
0.02588938 1
10.0%
0.02230486 1
10.0%
0.0204996 1
10.0%
0.01604573 1
10.0%
0.01253976 1
10.0%

sma_7
Numeric time series

High correlation  Non stationary 

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98831.556
Minimum98830.214
Maximum98839.029
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-18T08:26:39.406056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98830.214
5-th percentile98830.221
Q198830.232
median98830.243
Q398830.254
95-th percentile98837.049
Maximum98839.029
Range8.8142857
Interquartile range (IQR)0.021428571

Descriptive statistics

Standard deviation2.9662688
Coefficient of variation (CV)3.0013378 × 10-5
Kurtosis4.7717854
Mean98831.556
Median Absolute Deviation (MAD)0.014285714
Skewness2.2775939
Sum988315.56
Variance8.7987506
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9982218328
2025-05-18T08:26:39.424557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
2025-05-18T08:26:39.480182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-18T08:26:39.493469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98830.24286 3
30.0%
98839.02857 1
 
10.0%
98834.62857 1
 
10.0%
98830.22857 1
 
10.0%
98830.21429 1
 
10.0%
98830.22857 1
 
10.0%
98830.24286 1
 
10.0%
98830.25714 1
 
10.0%
ValueCountFrequency (%)
98830.21429 1
 
10.0%
98830.22857 1
 
10.0%
98830.22857 1
 
10.0%
98830.24286 3
30.0%
98830.24286 1
 
10.0%
98830.25714 1
 
10.0%
98834.62857 1
 
10.0%
98839.02857 1
 
10.0%
ValueCountFrequency (%)
98839.02857 1
 
10.0%
98834.62857 1
 
10.0%
98830.25714 1
 
10.0%
98830.24286 1
 
10.0%
98830.24286 3
30.0%
98830.22857 1
 
10.0%
98830.22857 1
 
10.0%
98830.21429 1
 
10.0%
2025-05-18T08:26:39.437325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

sma_14
Numeric time series

High correlation  Non stationary  Uniform  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98839.303
Minimum98830.243
Maximum98848.543
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-18T08:26:39.518277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98830.243
5-th percentile98831.23
Q198835.182
median98838.793
Q398843.729
95-th percentile98847.582
Maximum98848.543
Range18.3
Interquartile range (IQR)8.5464286

Descriptive statistics

Standard deviation6.0406092
Coefficient of variation (CV)6.1115457 × 10-5
Kurtosis-1.0625346
Mean98839.303
Median Absolute Deviation (MAD)4.8214286
Skewness0.065309636
Sum988393.03
Variance36.488959
MonotonicityStrictly decreasing
Augmented Dickey-Fuller test p-value0.4037936091
2025-05-18T08:26:39.541243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-18T08:26:39.703947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-18T08:26:39.718700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98848.54286 1
10.0%
98846.40714 1
10.0%
98844.27857 1
10.0%
98842.07857 1
10.0%
98839.88571 1
10.0%
98837.7 1
10.0%
98836.82143 1
10.0%
98834.63571 1
10.0%
98832.43571 1
10.0%
98830.24286 1
10.0%
ValueCountFrequency (%)
98830.24286 1
10.0%
98832.43571 1
10.0%
98834.63571 1
10.0%
98836.82143 1
10.0%
98837.7 1
10.0%
98839.88571 1
10.0%
98842.07857 1
10.0%
98844.27857 1
10.0%
98846.40714 1
10.0%
98848.54286 1
10.0%
ValueCountFrequency (%)
98848.54286 1
10.0%
98846.40714 1
10.0%
98844.27857 1
10.0%
98842.07857 1
10.0%
98839.88571 1
10.0%
98837.7 1
10.0%
98836.82143 1
10.0%
98834.63571 1
10.0%
98832.43571 1
10.0%
98830.24286 1
10.0%
2025-05-18T08:26:39.556817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

sma_21
Numeric time series

High correlation  Non stationary  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98841.456
Minimum98837.719
Maximum98844.79
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-18T08:26:39.743979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98837.719
5-th percentile98838.366
Q198839.852
median98841.505
Q398843.154
95-th percentile98844.368
Maximum98844.79
Range7.0714286
Interquartile range (IQR)3.3011905

Descriptive statistics

Standard deviation2.2477545
Coefficient of variation (CV)2.2741009 × 10-5
Kurtosis-0.86009497
Mean98841.456
Median Absolute Deviation (MAD)1.8928571
Skewness-0.14965113
Sum988414.56
Variance5.0524001
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.4105873795
2025-05-18T08:26:39.766356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-18T08:26:39.824784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-18T08:26:39.837896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98837.71905 1
10.0%
98839.15714 1
10.0%
98840.59524 1
10.0%
98841.99048 1
10.0%
98843.39048 1
10.0%
98844.79048 1
10.0%
98843.85238 1
10.0%
98842.44286 1
10.0%
98841.01905 1
10.0%
98839.60476 1
10.0%
ValueCountFrequency (%)
98837.71905 1
10.0%
98839.15714 1
10.0%
98839.60476 1
10.0%
98840.59524 1
10.0%
98841.01905 1
10.0%
98841.99048 1
10.0%
98842.44286 1
10.0%
98843.39048 1
10.0%
98843.85238 1
10.0%
98844.79048 1
10.0%
ValueCountFrequency (%)
98844.79048 1
10.0%
98843.85238 1
10.0%
98843.39048 1
10.0%
98842.44286 1
10.0%
98841.99048 1
10.0%
98841.01905 1
10.0%
98840.59524 1
10.0%
98839.60476 1
10.0%
98839.15714 1
10.0%
98837.71905 1
10.0%
2025-05-18T08:26:39.781570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

sma_60
Numeric time series

High correlation  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98831.654
Minimum98829.382
Maximum98833.778
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-18T08:26:39.860261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98829.382
5-th percentile98829.612
Q198830.535
median98831.686
Q398832.807
95-th percentile98833.62
Maximum98833.778
Range4.3966667
Interquartile range (IQR)2.2729167

Descriptive statistics

Standard deviation1.5052114
Coefficient of variation (CV)1.5230053 × 10-5
Kurtosis-1.2663767
Mean98831.654
Median Absolute Deviation (MAD)1.2625
Skewness-0.07406875
Sum988316.54
Variance2.2656612
MonotonicityStrictly decreasing
Augmented Dickey-Fuller test p-value0.000517397163
2025-05-18T08:26:39.878032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-18T08:26:39.932418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-18T08:26:39.944736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
98833.77833 1
10.0%
98833.42667 1
10.0%
98832.93167 1
10.0%
98832.435 1
10.0%
98831.94167 1
10.0%
98831.43 1
10.0%
98830.91833 1
10.0%
98830.40667 1
10.0%
98829.89333 1
10.0%
98829.38167 1
10.0%
ValueCountFrequency (%)
98829.38167 1
10.0%
98829.89333 1
10.0%
98830.40667 1
10.0%
98830.91833 1
10.0%
98831.43 1
10.0%
98831.94167 1
10.0%
98832.435 1
10.0%
98832.93167 1
10.0%
98833.42667 1
10.0%
98833.77833 1
10.0%
ValueCountFrequency (%)
98833.77833 1
10.0%
98833.42667 1
10.0%
98832.93167 1
10.0%
98832.435 1
10.0%
98831.94167 1
10.0%
98831.43 1
10.0%
98830.91833 1
10.0%
98830.40667 1
10.0%
98829.89333 1
10.0%
98829.38167 1
10.0%
2025-05-18T08:26:39.892153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

rsi_7
Numeric time series

High correlation 

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.795699
Minimum0.32258065
Maximum66.666667
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-18T08:26:39.965312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.32258065
5-th percentile0.32258065
Q18.8172043
median50
Q357.5
95-th percentile66.666667
Maximum66.666667
Range66.344086
Interquartile range (IQR)48.682796

Descriptive statistics

Standard deviation27.518632
Coefficient of variation (CV)0.72808897
Kurtosis-1.4665402
Mean37.795699
Median Absolute Deviation (MAD)16.666667
Skewness-0.61365609
Sum377.95699
Variance757.27509
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.003896642068
2025-05-18T08:26:39.985200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
2025-05-18T08:26:40.041242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-18T08:26:40.053424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
50 3
30.0%
0.3225806452 2
20.0%
66.66666667 2
20.0%
0.6451612904 1
 
10.0%
33.33333333 1
 
10.0%
60 1
 
10.0%
ValueCountFrequency (%)
0.3225806452 2
20.0%
0.6451612904 1
 
10.0%
33.33333333 1
 
10.0%
50 3
30.0%
60 1
 
10.0%
66.66666667 2
20.0%
ValueCountFrequency (%)
66.66666667 2
20.0%
60 1
 
10.0%
50 3
30.0%
33.33333333 1
 
10.0%
0.6451612904 1
 
10.0%
0.3225806452 2
20.0%
2025-05-18T08:26:39.999726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

rsi_14
Real number (ℝ)

High correlation  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.882066
Minimum0.95541401
Maximum37.625755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size160.0 B
2025-05-18T08:26:40.072700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.95541401
5-th percentile1.0969063
Q17.7936493
median27.479809
Q328.39552
95-th percentile33.569888
Maximum37.625755
Range36.670341
Interquartile range (IQR)20.601871

Descriptive statistics

Standard deviation13.935707
Coefficient of variation (CV)0.66735288
Kurtosis-1.1710182
Mean20.882066
Median Absolute Deviation (MAD)1.0294478
Skewness-0.82873325
Sum208.82066
Variance194.20392
MonotonicityNot monotonic
2025-05-18T08:26:40.091558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
28.61271676 1
10.0%
28.36468886 1
10.0%
28.4057971 1
10.0%
27.35294118 1
10.0%
27.45961821 1
10.0%
27.5 1
10.0%
37.62575453 1
10.0%
1.27388535 1
10.0%
0.9554140128 1
10.0%
1.26984127 1
10.0%
ValueCountFrequency (%)
0.9554140128 1
10.0%
1.26984127 1
10.0%
1.27388535 1
10.0%
27.35294118 1
10.0%
27.45961821 1
10.0%
27.5 1
10.0%
28.36468886 1
10.0%
28.4057971 1
10.0%
28.61271676 1
10.0%
37.62575453 1
10.0%
ValueCountFrequency (%)
37.62575453 1
10.0%
28.61271676 1
10.0%
28.4057971 1
10.0%
28.36468886 1
10.0%
27.5 1
10.0%
27.45961821 1
10.0%
27.35294118 1
10.0%
1.27388535 1
10.0%
1.26984127 1
10.0%
0.9554140128 1
10.0%

rsi_21
Numeric time series

High correlation  Non stationary 

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.945104
Minimum28.489209
Maximum68.292683
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-18T08:26:40.114459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum28.489209
5-th percentile28.553957
Q130.95424
median61.433574
Q361.627629
95-th percentile65.320264
Maximum68.292683
Range39.803474
Interquartile range (IQR)30.67339

Descriptive statistics

Standard deviation16.750606
Coefficient of variation (CV)0.33538034
Kurtosis-1.9899566
Mean49.945104
Median Absolute Deviation (MAD)3.5564205
Skewness-0.50253975
Sum499.45104
Variance280.5828
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.8903577838
2025-05-18T08:26:40.137298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
2025-05-18T08:26:40.195721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-18T08:26:40.210235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
61.6873065 2
20.0%
61.44859813 2
20.0%
68.29268293 1
10.0%
61.41855027 1
10.0%
37.61006289 1
10.0%
28.73563218 1
10.0%
28.48920863 1
10.0%
28.63309353 1
10.0%
ValueCountFrequency (%)
28.48920863 1
10.0%
28.63309353 1
10.0%
28.73563218 1
10.0%
37.61006289 1
10.0%
61.41855027 1
10.0%
61.44859813 2
20.0%
61.6873065 2
20.0%
68.29268293 1
10.0%
ValueCountFrequency (%)
68.29268293 1
10.0%
61.6873065 2
20.0%
61.44859813 2
20.0%
61.41855027 1
10.0%
37.61006289 1
10.0%
28.73563218 1
10.0%
28.63309353 1
10.0%
28.48920863 1
10.0%
2025-05-18T08:26:40.152470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

rsi_60
Numeric time series

High correlation 

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.56075
Minimum44.726027
Maximum50.61237
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-18T08:26:40.234349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum44.726027
5-th percentile44.732922
Q144.74135
median44.829813
Q344.944972
95-th percentile48.76322
Maximum50.61237
Range5.8863425
Interquartile range (IQR)0.20362177

Descriptive statistics

Standard deviation1.8549899
Coefficient of variation (CV)0.040714649
Kurtosis7.7297443
Mean45.56075
Median Absolute Deviation (MAD)0.096060014
Skewness2.7466178
Sum455.6075
Variance3.4409876
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0004738413895
2025-05-18T08:26:40.253067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
2025-05-18T08:26:40.308885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-18T08:26:40.320923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
44.74134978 3
30.0%
50.61236987 1
 
10.0%
46.50314882 1
 
10.0%
44.93346981 1
 
10.0%
44.91467577 1
 
10.0%
44.94880546 1
 
10.0%
44.7260274 1
 
10.0%
44.74495036 1
 
10.0%
ValueCountFrequency (%)
44.7260274 1
 
10.0%
44.74134978 3
30.0%
44.74495036 1
 
10.0%
44.91467577 1
 
10.0%
44.93346981 1
 
10.0%
44.94880546 1
 
10.0%
46.50314882 1
 
10.0%
50.61236987 1
 
10.0%
ValueCountFrequency (%)
50.61236987 1
 
10.0%
46.50314882 1
 
10.0%
44.94880546 1
 
10.0%
44.93346981 1
 
10.0%
44.91467577 1
 
10.0%
44.74495036 1
 
10.0%
44.74134978 3
30.0%
44.7260274 1
 
10.0%
2025-05-18T08:26:40.266717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

macd_7
Real number (ℝ)

High correlation  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.92892436
Minimum-1.3488453
Maximum-0.41492144
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)100.0%
Memory size160.0 B
2025-05-18T08:26:40.340553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.3488453
5-th percentile-1.3298209
Q1-1.1589006
median-0.93727242
Q3-0.70789458
95-th percentile-0.48288891
Maximum-0.41492144
Range0.93392391
Interquartile range (IQR)0.45100607

Descriptive statistics

Standard deviation0.31547262
Coefficient of variation (CV)-0.33961066
Kurtosis-1.0809004
Mean-0.92892436
Median Absolute Deviation (MAD)0.24629381
Skewness0.22955318
Sum-9.2892436
Variance0.099522974
MonotonicityNot monotonic
2025-05-18T08:26:40.358116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-0.4149214436 1
10.0%
-0.8976441955 1
10.0%
-1.130137665 1
10.0%
-1.306568813 1
10.0%
-1.348845349 1
10.0%
-1.168488311 1
10.0%
-0.9769006475 1
10.0%
-0.8038762706 1
10.0%
-0.6759006822 1
10.0%
-0.5659602644 1
10.0%
ValueCountFrequency (%)
-1.348845349 1
10.0%
-1.306568813 1
10.0%
-1.168488311 1
10.0%
-1.130137665 1
10.0%
-0.9769006475 1
10.0%
-0.8976441955 1
10.0%
-0.8038762706 1
10.0%
-0.6759006822 1
10.0%
-0.5659602644 1
10.0%
-0.4149214436 1
10.0%
ValueCountFrequency (%)
-0.4149214436 1
10.0%
-0.5659602644 1
10.0%
-0.6759006822 1
10.0%
-0.8038762706 1
10.0%
-0.8976441955 1
10.0%
-0.9769006475 1
10.0%
-1.130137665 1
10.0%
-1.168488311 1
10.0%
-1.306568813 1
10.0%
-1.348845349 1
10.0%

macdsignal_7
Real number (ℝ)

High correlation  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.487992
Minimum9.1788265
Maximum11.600012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size160.0 B
2025-05-18T08:26:40.375199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.1788265
5-th percentile9.3773894
Q19.9361933
median10.434161
Q310.993201
95-th percentile11.58759
Maximum11.600012
Range2.4211853
Interquartile range (IQR)1.0570076

Descriptive statistics

Standard deviation0.80582076
Coefficient of variation (CV)0.076832703
Kurtosis-0.8564184
Mean10.487992
Median Absolute Deviation (MAD)0.59604962
Skewness-0.08492324
Sum104.87992
Variance0.6493471
MonotonicityNot monotonic
2025-05-18T08:26:40.391886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9.178826548 1
10.0%
9.823276307 1
10.0%
10.45391367 1
10.0%
10.41440833 1
10.0%
11.01537555 1
10.0%
11.57240671 1
10.0%
11.60001183 1
10.0%
10.92667727 1
10.0%
10.27494448 1
10.0%
9.62007722 1
10.0%
ValueCountFrequency (%)
9.178826548 1
10.0%
9.62007722 1
10.0%
9.823276307 1
10.0%
10.27494448 1
10.0%
10.41440833 1
10.0%
10.45391367 1
10.0%
10.92667727 1
10.0%
11.01537555 1
10.0%
11.57240671 1
10.0%
11.60001183 1
10.0%
ValueCountFrequency (%)
11.60001183 1
10.0%
11.57240671 1
10.0%
11.01537555 1
10.0%
10.92667727 1
10.0%
10.45391367 1
10.0%
10.41440833 1
10.0%
10.27494448 1
10.0%
9.823276307 1
10.0%
9.62007722 1
10.0%
9.178826548 1
10.0%

macdhist_7
Real number (ℝ)

High correlation  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.416916
Minimum-12.740895
Maximum-9.593748
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)100.0%
Memory size160.0 B
2025-05-18T08:26:40.407164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-12.740895
5-th percentile-12.667103
Q1-12.205804
median-11.652514
Q3-10.778402
95-th percentile-9.8602783
Maximum-9.593748
Range3.147147
Interquartile range (IQR)1.4274024

Descriptive statistics

Standard deviation1.0411153
Coefficient of variation (CV)-0.09119059
Kurtosis-0.74777497
Mean-11.416916
Median Absolute Deviation (MAD)0.81805245
Skewness0.43912179
Sum-114.16916
Variance1.0839211
MonotonicityNot monotonic
2025-05-18T08:26:40.425747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-9.593747991 1
10.0%
-10.7209205 1
10.0%
-11.58405134 1
10.0%
-11.72097714 1
10.0%
-12.3642209 1
10.0%
-12.74089502 1
10.0%
-12.57691248 1
10.0%
-11.73055354 1
10.0%
-10.95084516 1
10.0%
-10.18603748 1
10.0%
ValueCountFrequency (%)
-12.74089502 1
10.0%
-12.57691248 1
10.0%
-12.3642209 1
10.0%
-11.73055354 1
10.0%
-11.72097714 1
10.0%
-11.58405134 1
10.0%
-10.95084516 1
10.0%
-10.7209205 1
10.0%
-10.18603748 1
10.0%
-9.593747991 1
10.0%
ValueCountFrequency (%)
-9.593747991 1
10.0%
-10.18603748 1
10.0%
-10.7209205 1
10.0%
-10.95084516 1
10.0%
-11.58405134 1
10.0%
-11.72097714 1
10.0%
-11.73055354 1
10.0%
-12.3642209 1
10.0%
-12.57691248 1
10.0%
-12.74089502 1
10.0%

obv_7
Real number (ℝ)

High correlation  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10331377
Minimum0.01253976
Maximum0.50743402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size160.0 B
2025-05-18T08:26:40.443632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01253976
5-th percentile0.014117447
Q10.020950915
median0.030536955
Q30.13005896
95-th percentile0.35457085
Maximum0.50743402
Range0.49489426
Interquartile range (IQR)0.10910804

Descriptive statistics

Standard deviation0.15273461
Coefficient of variation (CV)1.4783567
Kurtosis6.4757983
Mean0.10331377
Median Absolute Deviation (MAD)0.01624421
Skewness2.4574753
Sum1.0331377
Variance0.02332786
MonotonicityNot monotonic
2025-05-18T08:26:40.462657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0204996 1
10.0%
0.01604573 1
10.0%
0.02588938 1
10.0%
0.03518453 1
10.0%
0.50743402 1
10.0%
0.16773809 1
10.0%
0.07813467 1
10.0%
0.14736705 1
10.0%
0.02230486 1
10.0%
0.01253976 1
10.0%
ValueCountFrequency (%)
0.01253976 1
10.0%
0.01604573 1
10.0%
0.0204996 1
10.0%
0.02230486 1
10.0%
0.02588938 1
10.0%
0.03518453 1
10.0%
0.07813467 1
10.0%
0.14736705 1
10.0%
0.16773809 1
10.0%
0.50743402 1
10.0%
ValueCountFrequency (%)
0.50743402 1
10.0%
0.16773809 1
10.0%
0.14736705 1
10.0%
0.07813467 1
10.0%
0.03518453 1
10.0%
0.02588938 1
10.0%
0.02230486 1
10.0%
0.0204996 1
10.0%
0.01604573 1
10.0%
0.01253976 1
10.0%

Interactions

2025-05-18T08:26:38.461549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:33.536321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:33.927050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.212802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.531479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.810233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.204210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.543861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.878679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.162596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.460239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.908225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.209229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.488584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.761637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.035895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.480335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:33.572422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-18T08:26:34.931518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-18T08:26:37.795483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.076960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.521101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-18T08:26:38.537775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-18T08:26:37.662001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-18T08:26:38.347571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.663382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:33.835111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.126935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.431676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.726024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.106227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.437828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.775407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.075758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.368980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.811652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.119591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.403075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.680244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.952790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.367252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.678661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:33.852927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.144401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.451267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.742193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.125742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.459812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.797241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.093938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.385970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.834505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.137008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.418646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.697888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.968898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.386653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.694405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:33.871459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.161876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.470377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.758848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.141930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.480053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.817310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.109757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.403304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.851030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.154981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.436023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.713030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.984999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.404961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.711680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:33.889163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.178741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.491279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.775735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.163559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.500058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.836286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.126226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.422840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.869142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.172469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.453924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.729358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.001065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.424577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.729190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:33.908510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.195926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.512074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:34.794404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.183432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.524444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:35.858777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.145589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.441731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:36.889801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.191357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.472799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:37.746317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.020107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T08:26:38.442664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-18T08:26:40.484411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
closelowmacd_7macdhist_7macdsignal_7obv_7openrsi_14rsi_21rsi_60rsi_7sma_14sma_21sma_60sma_7targetvolumewindow_start_ms
close1.0000.0000.0000.0000.3870.1580.2990.0000.0000.0000.1581.0000.0000.0000.0000.0000.1581.000
low0.0001.0000.3540.6120.6120.0000.0000.2040.0000.0000.7911.0000.0000.3540.0000.2040.0001.000
macd_70.0000.3541.0000.733-0.685-0.721-0.114-0.127-0.177-0.031-0.241-0.188-0.648-0.1880.7060.567-0.7210.188
macdhist_70.0000.6120.7331.000-0.976-0.903-0.038-0.1270.1520.472-0.5370.224-0.9760.2240.4850.175-0.903-0.224
macdsignal_70.3870.612-0.685-0.9761.0000.8670.0380.224-0.140-0.4600.463-0.2240.939-0.224-0.497-0.0750.8670.224
obv_70.1580.000-0.721-0.9030.8671.000-0.3420.079-0.018-0.2760.537-0.0790.879-0.079-0.522-0.1501.0000.079
open0.2990.000-0.114-0.0380.038-0.3421.000-0.038-0.229-0.346-0.155-0.1900.038-0.190-0.115-0.234-0.3420.190
rsi_140.0000.204-0.127-0.1270.2240.079-0.0381.0000.7380.472-0.4010.673-0.0180.6730.166-0.4110.079-0.673
rsi_210.0000.000-0.1770.152-0.140-0.018-0.2290.7381.0000.827-0.4910.945-0.3230.9450.210-0.677-0.018-0.945
rsi_600.0000.000-0.0310.472-0.460-0.276-0.3460.4720.8271.000-0.4750.828-0.5950.8280.230-0.461-0.276-0.828
rsi_70.1580.791-0.241-0.5370.4630.537-0.155-0.401-0.491-0.4751.000-0.6480.673-0.648-0.0380.2600.5370.648
sma_141.0001.000-0.1880.224-0.224-0.079-0.1900.6730.9450.828-0.6481.000-0.3941.0000.092-0.729-0.079-1.000
sma_210.0000.000-0.648-0.9760.9390.8790.038-0.018-0.323-0.5950.673-0.3941.000-0.394-0.460-0.0440.8790.394
sma_600.0000.354-0.1880.224-0.224-0.079-0.1900.6730.9450.828-0.6481.000-0.3941.0000.092-0.729-0.079-1.000
sma_70.0000.0000.7060.485-0.497-0.522-0.1150.1660.2100.230-0.0380.092-0.4600.0921.0000.063-0.522-0.092
target0.0000.2040.5670.175-0.075-0.150-0.234-0.411-0.677-0.4610.260-0.729-0.044-0.7290.0631.000-0.1500.729
volume0.1580.000-0.721-0.9030.8671.000-0.3420.079-0.018-0.2760.537-0.0790.879-0.079-0.522-0.1501.0000.079
window_start_ms1.0001.0000.188-0.2240.2240.0790.190-0.673-0.945-0.8280.648-1.0000.394-1.000-0.0920.7290.0791.000

Missing values

2025-05-18T08:26:38.761372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-18T08:26:38.795951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

window_start_msopenhighlowclosetargetvolumesma_7sma_14sma_21sma_60rsi_7rsi_14rsi_21rsi_60macd_7macdsignal_7macdhist_7obv_7
1746685620000174668562000098830.2098830.3098830.2098830.3098830.300.0298839.0398848.5498837.7298833.780.6528.6168.2950.61-0.419.18-9.590.02
1746685680000174668568000098830.3098830.3098830.2098830.2098830.200.0298834.6398846.4198839.1698833.430.3228.3661.6946.50-0.909.82-10.720.02
1746685740000174668574000098830.3098830.3098830.2098830.2098830.300.0398830.2398844.2898840.6098832.930.3228.4161.6944.93-1.1310.45-11.580.03
1746685800000174668580000098830.3098830.3098830.2098830.2098830.200.0498830.2198842.0898841.9998832.4333.3327.3561.4244.91-1.3110.41-11.720.04
1746685860000174668586000098830.2098830.3098830.2098830.3098830.300.5198830.2398839.8998843.3998831.9466.6727.4661.4544.95-1.3511.02-12.360.51
1746685920000174668592000098830.3098830.3098830.2098830.3098830.200.1798830.2498837.7098844.7998831.4366.6727.5061.4544.74-1.1711.57-12.740.17
1746685980000174668598000098830.3098830.3098830.2098830.2098847.300.0898830.2498836.8298843.8598830.9250.0037.6337.6144.74-0.9811.60-12.580.08
1746686040000174668604000098830.2098830.3098830.2098830.3098850.000.1598830.2498834.6498842.4498830.4150.001.2728.7444.74-0.8010.93-11.730.15
1746686100000174668610000098830.3098830.3098830.2098830.2098849.900.0298830.2498832.4498841.0298829.8950.000.9628.4944.73-0.6810.27-10.950.02
1746686160000174668616000098830.3098830.3098830.3098830.3098850.000.0198830.2698830.2498839.6098829.3860.001.2728.6344.74-0.579.62-10.190.01
window_start_msopenhighlowclosetargetvolumesma_7sma_14sma_21sma_60rsi_7rsi_14rsi_21rsi_60macd_7macdsignal_7macdhist_7obv_7
1746685620000174668562000098830.2098830.3098830.2098830.3098830.300.0298839.0398848.5498837.7298833.780.6528.6168.2950.61-0.419.18-9.590.02
1746685680000174668568000098830.3098830.3098830.2098830.2098830.200.0298834.6398846.4198839.1698833.430.3228.3661.6946.50-0.909.82-10.720.02
1746685740000174668574000098830.3098830.3098830.2098830.2098830.300.0398830.2398844.2898840.6098832.930.3228.4161.6944.93-1.1310.45-11.580.03
1746685800000174668580000098830.3098830.3098830.2098830.2098830.200.0498830.2198842.0898841.9998832.4333.3327.3561.4244.91-1.3110.41-11.720.04
1746685860000174668586000098830.2098830.3098830.2098830.3098830.300.5198830.2398839.8998843.3998831.9466.6727.4661.4544.95-1.3511.02-12.360.51
1746685920000174668592000098830.3098830.3098830.2098830.3098830.200.1798830.2498837.7098844.7998831.4366.6727.5061.4544.74-1.1711.57-12.740.17
1746685980000174668598000098830.3098830.3098830.2098830.2098847.300.0898830.2498836.8298843.8598830.9250.0037.6337.6144.74-0.9811.60-12.580.08
1746686040000174668604000098830.2098830.3098830.2098830.3098850.000.1598830.2498834.6498842.4498830.4150.001.2728.7444.74-0.8010.93-11.730.15
1746686100000174668610000098830.3098830.3098830.2098830.2098849.900.0298830.2498832.4498841.0298829.8950.000.9628.4944.73-0.6810.27-10.950.02
1746686160000174668616000098830.3098830.3098830.3098830.3098850.000.0198830.2698830.2498839.6098829.3860.001.2728.6344.74-0.579.62-10.190.01